Efficient location aware intrusion detection to protect mobile devices

Authors:
Sausan Yazji;Peter Scheuermann;Robert P. Dick;Goce Trajcevski;Ruoming Jin
Affiliations:
EECS Department, Northwestern University, Evanston, USA 60208;EECS Department, Northwestern University, Evanston, USA 60208;EECS Department, University of Michigan, Ann Arbor, USA 48109;EECS Department, Northwestern University, Evanston, USA 60208;CS Department, Kent State University, Kent, USA 44242
Venue:
Personal and Ubiquitous Computing
Year:
2014

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Abstract

This paper addresses the problem of efficient intrusion detection for mobile devices via correlating the user's location and time data. We developed two statistical profiling approaches for modeling the normal spatio---temporal behavior of the users: one based on an empirical cumulative probability measure and the other based on the Markov properties of trajectories. An anomaly is detected when the probability of a particular (location, time) evolution matching the normal behavior of a given user becomes lower than a certain threshold, determined by controlling the recall rate of the model of the normal user's behavior. We used compression techniques to reduce processing overhead while maintaining high accuracy. Our evaluation based on the Reality Mining and Geolife data sets shows that the proposed system is capable of detecting a potential intrusion within 15 min and with 94 % accuracy.